NNI - 微软亚洲研究院开源的轻量级AutoML工具包

NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments. The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.

Who should consider using NNI

Those who want to try different AutoML algorithms in their training code (model) at their local machine.

Those who want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud).

Researchers and data scientists who want to implement their own AutoML algorithms and compare it with other algorithms.

ML Platform owners who want to support AutoML in their platform.

Related Projects

Targeting at openness and advancing state-of-art technology, Microsoft Research (MSR) had also released few other open source projects.

OpenPAI : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.

FrameworkController : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.

MMdnn : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network. We encourage researchers and students leverage these projects to accelerate the AI development and research.

Install & Verify

Install through pip

We support Linux and MacOS in current stage, Ubuntu 16.04 or higher, along with MacOS 10.14.1 are tested and supported. Simply run the following pip install in an environment that has python >= 3.5.

python3 -m pip install --upgrade nni

Note:

--user can be added if you want to install NNI in your home directory, which does not require any special privileges.

The following example is an experiment built on TensorFlow. Make sure you have TensorFlow installed before running it.

Download the examples via clone the source code.

git clone -b v0.5.1 https://github.com/Microsoft/nni.git

Run the mnist example.

nnictl create --config nni/examples/trials/mnist/config.yml

Wait for the message INFO: Successfully started experiment! in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the Web UI url.